基于因子图的在线贝叶斯识别和组件评估,用于多变量自回归外源输入模型
Tim N Nisslbeck1, Wouter M Kouw1
1Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands.
Entropy (Basel, Switzerland)
|July 29, 2025
概括
我们介绍了多变量自回归模型的新型因子图,使在线贝叶斯参数识别成为可能. 这种方法有效地跟踪不确定性传播,以提高时间序列分析中的预测性能.
科学领域:
- 机器学习 机器学习
- 统计建模 统计建模
- 时间序列分析时间序列分析
背景情况:
- 具有外源输入的多变量自回归 (MAR) 模型被广泛用于时间序列分析.
- 准确的参数识别对于可靠的模型预测至关重要.
- 现有的方法可能难以在线更新和不确定性量化.
研究的目的:
- 在使用因子图的MAR模型中开发一个统一的参数识别框架.
- 提出基于消息传递的参数估计的在线贝叶斯程序.
- 分析模型中的不确定性传播及其对预测的影响.
主要方法:
- 为具有外源输入的 MAR 模型开发了一个 Forney 式的因子图表表示.
- 提出了一个在线贝叶斯参数识别程序,利用消息传递.
- 用于MAR概率和矩阵正常-维沙特分布的自定义因子节点被导出.
- 消息更新规则是为了跟踪参数不确定性和模型证据而制定的.
主要成果:
- 消息传递程序在模拟的自回归系统上显示了趋同.
- 该方法在基准任务上取得了强大的预测性能.
- 分析揭示了参数不确定性如何影响预测不确定性.
- 阐明了单个模型组件对整体证据的贡献.
结论:
- 提出的因子图和消息传递方法为MAR模型中的在线参数识别提供了有效的方法.
- 这一框架提高了不确定性量化和预测准确度.
- 该方法提供了对模型结构和证据贡献的见解.
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